Working Paper Series | 1 | 2015
Bank and sovereign risk
This paper studies the link between
sovereign risks and the fragility of the
banking sector pointing to the challenges
of bank rescue operations for the state.
This Working Paper should not be reported as representing the views of the ESM.
The views expressed in this Working Paper are those of the author(s) and do not
necessarily represent those of the ESM or ESM policy.
Working Paper Series | 1 | 2015
Bank and sovereign risk feedback loops
Aitor Erce 1
Measures of sovereign and bank risk show occasional bouts of increased correlation, setting the stage
for vicious and virtuous feedback loops. This paper models the macroeconomic phenomena underlying
such bouts using CDS data for 10 euro area countries. The results show that sovereign risk feeds back
into bank risk more strongly than vice versa. Countries with sovereigns that are more indebted or where
banks have a larger exposure to their own sovereign, suffer larger feedback loop effects from sovereign
risk into bank risk. In the opposite direction, in countries where banks fund their activities with more foreign
credit and support larger levels of non-performing loans, the feedback from bank risk into sovereign risk is
stronger. According to model estimates, financial rescue operations can increase feedback effects from
bank risk into sovereign risk. These results can be useful for the official sector when deciding on the form
of financial rescues.
Sovereign Risk, Bank Risk, Feedback Loops, Balance Sheet Exposure, Leverage
E58, G21, G28, H63
I thank Antonello D’Agostino, Gong Cheng, Jon Frost, Patricia Gomez, Carlos Martins, Tomasz Orpiszewski, Cheng PG-Yan, Chander
Ramaswamy, Juan Rojas, Karol Siskind and seminar participants at the European Stability Mechanism and the 2014 Symposium
of Economic Analysis for their suggestions, and Sarai Criado, Gabi Perez-Quiros and Adrian Van Rixtel for sharing their CDS data.
Assunta Di Chiara provided outstanding research assistance.
European Stability Mechanism. The author can be contacted at: firstname.lastname@example.org.
This Working Paper should not be reported as representing the views of the ESM. The views expressed in this Working Paper are
those of the author(s) and do not necessarily represent those of the ESM or ESM policy.
No responsibility or liability is accepted by the ESM in relation to the accuracy or completeness of the information, including any
data sets, presented in this Working Paper.
© European Stability Mechanism, 2015 All rights reserved. Any reproduction, publication and reprint in the form of a
different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written
authorisation of the European Stability Mechanism.
EU catalogue number DW-AB-15-001-EN-N
As the global crisis engulfed a number of economies into a perverse spiral of …scal
and …nancial distress, the interconnectedness between banks and sovereigns
has attracted increasing attention. On the one hand, a number of countries
faced severe banking crises, whose management contributed to the subsequent
…scal crisis. Arguably, this is what happened to Iceland, where the materialization
of contingent claims brought havoc onto the sovereign’s balance sheet. 1 On
the other hand, pro-cyclical …scal policy and a lack of competitiveness led to a
sovereign debt crisis in Greece. As foreign investors withdrew, banks became
major holders of public debt (Broner et al., 2014). Successive sovereign downgrades,
ending in a sovereign debt restructuring, contributed to the collapse
of the Greek banking sector. Against this background, this paper uses euro
area data to extract lessons about the processes through which sovereigns and
banks interlink. In order to do so, this paper provides a framework that relates
the joint dynamics of …scal credit risk (Sovereign Risk) and banking credit risk
(Bank Risk) to di¤erent underlying vulnerabilities and shocks. The analysis delivers
an understanding of what conditions facilitate the emergence of feedback
loops between sovereign and bank risk.
A number of recent contributions study this two-way relationship by modelling
the common dynamics of bank and sovereign Credit Default Swaps (CDS)
spreads using vector-auto regression models as in Diebold and Yilmaz (2009).
According to Moody’s (2014), which studies the dynamic relation between sovereign
and bank CDS spreads by means of a Markov switching VAR methodology,
the euro area did not su¤er one …nancial crisis, but a variety of crises,
each of them with its own speci…cities. According to their results, only Ireland
witnessed a spillover of …nancial stress into sovereign stress. Instead, for Greece
and Italy their results point to the opposite feedback e¤ect. For the rest of the
countries analyzed, stress feeds back in both directions. These time series techniques
deliver interesting indices of contagion but fall short of describing the
actual channels through which such bouts of contagion take place. To bridge
this gap, this paper provides a framework conditioning the intensity of the feedback
loops on di¤erent economic factors. In doing so, similar to Acharya et al.
(2013) or Mody and Sandri (2011), this paper delivers an understanding of the
vulnerabilities and shocks that are fertile ground for the emergence of vicious
spirals of increasing sovereign and bank risk. 2
To provide estimates of how credit risk interconnectedness varies with the
economic environment, the analysis uses detailed information on the state of
public …nances, the banking system and the macro economy. The paper presents
a simple econometric strategy to assess whether the sensibility of the feedback
between bank and sovereign risk varies with these indicators. Given the low frequency
of macroeconomic variables and the short time series available for CDS
1 In Iceland, bank failures directly increased net public debt by 13% of GDP (Carey, 2009).
2 Heinz and Sun (2014) or Delatte et al. (2014) show the presence of non-linearities on
sovereign risk pricing.
data, the paper relies on panel data econometrics. In addition to a generalisedleast-squares
estimator, motivated by the high persistence of the CDS series,
dynamic models are also used. The framework provides a quantitative benchmark
to measure the impact on sovereign risk of bank rescue measures, as those
enacted by euro area governments between 2007 and 2013. Understanding the
sensitivity of sovereign risk to such policies is, given the Euro Area policy setting,
The main …ndings are the following. There is a strong pass-through of
sovereign risk on bank risk. Moreover, the sovereign feedback e¤ect is quantitatively
stronger when increases in sovereign risk occur in countries with a
larger stock of public debt, when the banking system exposure to the sovereign
is large or when the sovereign has lost its investment grade rating. There is also
evidence of positive spillovers from bank risk into sovereign risk. In this case,
however, signi…cant pass-through appears only under speci…c macroeconomic
environments and is signi…cantly smaller. Bank risk spillovers are signi…cantly
stronger in countries where banks have bigger balance sheets and where the
volume of non-performing loans and foreign liabilities is larger. As regards the
role of bank rescues, the results show that such policy operations can facilitate
the appearance of strong feedback e¤ects.
The next section summarizes the main channels through which distress
spreads, as documented in the literature. The following one describes the data
and presents some preliminary evidence. The next describes the econometric
strategy and details the main results from the analysis. The section also
presents a detailed analysis of the e¤ect on the feedback between risks of the
bank bailouts designed in Europe during the crisis. The …nal section concludes.
1 Literature review: what are the channels of
In order to guide the analysis and help clarifying the choice of variables for
carrying out the empirical exercise, this section discusses the most relevant
channels through which …nancial and …scal stress intertwine, as identi…ed in
literature. 4 These channels include the direct balance sheet interconnection, as
well as other indirect ways through which underlying vulnerabilities in either
the banking or public sector may materialize into twin crises.
3 The European Banking Union aims to delink sovereigns and banks by allowing for bank
recapitalisation funded at the European level whenever a bank rescue risks overburdening the
national …scal position.
4 Reinhart and Rogo¤ (2012) show that (i) private and public debt booms ahead of banking
crises, (ii) banking crises, both home-grown and imported, often accompany sovereign debt
crises and, (iii) public borrowing increases sharply ahead of debt crises and (iv) it turns out
that the government has “hidden debts” (domestic public debt and contingent private debt).
Closely related, Balteanu and Erce (2014) show that twin sovereign debt and banking crises
in emerging countries always combine with boom-bust patterns on the banking system.
A number of recent contributions study the two-way feedback between Sovereign
and Bank stress by studying the common dynamics of bank and sovereign
CDS spreads using vector-auto regression models (following the methodology
proposed by Diebold and Yilmaz (2009). While these models are extremely
useful to understand the joint dynamics of the series, as they rely fully on the
time series dimension, they provide no economic guidance on the drivers of the
feedback e¤ects. In order to gauge an idea on the speci…c mechanisms through
which stress transmits, the literature has relied, instead, on pooling country
data together. Heinz and Sun (2014) use a generalized least squares panel data
approach to analyze sovereign CDS drivers. They show that global factors account
for a relevant portion of the observed variation. Acharya et al. (2013)
present cross-country evidence about the potential for bank bailouts to trigger
a …scal crisis. Their narrative of the crisis presents three di¤erentiated periods.
They portray a …rst period, extending until 2007, in which sovereign risk
was never an issue within the euro area. Then, starting with the …rst bank
bailouts in 2008, sovereign risk starts to surface in some parts of the Monetary
Union as economic prospects deteriorate and public debt raises on the back of
the support provided to a seriously deteriorated …nancial system. Since 2010,
sovereign risk has become the major concern and, for some countries, implied a
resurfacing of concerns regarding …nancial risk, due to the fact that a number of
banks were either heavily exposed to the sovereign (Bruegel, 2012) or su¤ered
from the lowering of the public guarantees provided to them (BIS, 2010). The
empirical analysis in Acharya et al. (2013) relies on the use of CDS spreads and
relates their co-movement to resolution policies and macro factors. Their results
show that the bailout led to an increase in sovereign risk. Moreover, they show
that, even after controlling for bank-speci…c and macroeconomic variables, the
contemporaneous relation between sovereign and bank CDS spreads remain,
con…rming the existence of a sovereign bank loop. Closely related, Thukral
(2013) uses a panel data framework with lagged regressors to study the role of
…nancial sector variables on the determination of sovereign CDS spreads. He
constructs a bank risk index using bank CDS spreads and …nds that the index is
the primarily statistically signi…cant determinant of sovereign risk premia even
when …scal variable are included, which he characterizes as bank dominance of
sovereign …nancing conditions. Mody and Sandri (2011) recognize the existence
of broadly similar sub-periods as Acharya et al. (2013), in which the feedback
between sovereign and bank risk changed. Instead of comparing CDS spreads,
Mody and Sandri (2011) focus on sovereign spreads as a measure of the …scal
risk, and banks’ stock market capitalization as a measure of risk within the
banking system. Their results, using spreads and market valuations, show that
the euro crisis traces back to the demise of Bear Stearns. They argue that under
the weight of increasing support for banks, sovereign spreads started to rise,
especially in countries with weak growth prospects and high debt levels.
Another literature strand has delved into the role of monetary policy in
strengthening the vicious relation between sovereign and bank risk. According
to Darraq-Pires et al. (2013), the ECB’s full-allotment liquidity policy is an
e¢ cient tool to stabilize spiralling feedback loops between banks and the …scal
authorities. Drechsler et al. (2013) study the reasons behind the heterogeneous
take up of long-term re…nancing operations (LTROs) among European banks.
They document that banks where this take up was larger also featured larger
increases in their sovereign debt exposure. 5 Drechsler et al. (2013) de…ne a
haircut subsidy associated with using government bonds as collateral with the
ECB, as opposed to government bonds in private repo markets. Using this
subsidy, they provide support for the hypothesis that ECB collateral policies
action help explain the increased balance sheet interconnection between banks
and sovereigns in the euro area.
As regards the main transmission channels from bank stress to the sovereign,
Candelon and Palm (2010) highlight four. First, rescue plans may impair the
sustainability of public …nances. 6 They can include bailout money, government
deposits, liquidity provisioning by the central bank, public recapitalization and
the materialization of public guarantees. 7 Second, if contingent liabilities materialize,
…scal costs are likely to be substantial. Next, the risk premium increases
even if guarantees remain unused, raising borrowing costs for both the sovereign
and the private sector (sovereign ceiling). 8 Last, the downturn originated by the
credit crunch accompanying the …nancial crisis can deepen the recession, leading
to further falls in public revenues, deepening the de…cit and driving up debt.
King (2009) provides an event analysis on the impact of government guarantees
on the banking system using the battery of bank rescues that took place in late
2008. According to his results, the bailouts bene…ted the banks’creditors, as
re‡ected in falling bank CDS spreads, at the expense of equity holders, given
that banks’stock underperformed vis-a-vis the market.
If …nancial turmoil negatively in‡uences asset prices, unemployment and
output, the direct costs increase by the impact of the crisis on tax collection and
public expenditure. Baldacci and Gupta (2009a, 2009b) argue that sovereign
debt distress (deterioration of the …scal position) after a banking crisis is likely
to occur due to a combination of lower revenues and higher expenditures (bank
rescues and outlays associated with the downturn). 9 According to Honohan
(2008), banking crises last 2.5 years on average, public debt increases by around
30% of GDP and their estimated median …scal cost stands at 15.5% of GDP.
Distress can also spread through the credit crunch created by the …nancial
5 Acharya and Tuckman (2013), using data for broker-dealers in the US, show that Lender of
Last Resort activities can have the perverse side e¤ect of slowing down deleveraging, increasing
illiquid leverage and the risk of default.
6 Rosas (2006) studies the drivers of government intervention after banking crises. He …nds
that authorities are more likely to bailout failing institutions in open and rich economies or
if …nancial turmoil was caused by regulatory issues. On the other hand, electoral constraints
and central bank independence seem to favor bank closure.
7 See Feenstra and Taylor (2008) or Reinhart and Rogo¤ (2011).
8 Laeven and Valencia (2011) show that blanket guarantees increase the …scal costs of
banking crises, but this can also be because they are set in place during severe crises.
9 Baldacci and Gupta (2009) argue that …scal expansions do not improve the growth outlook
by themselves and lead to higher interest rates on long-term government debt. They identify a
trade-o¤ between boosting aggregate demand (short-run) and productivity growth (long run).
crisis. As credit falls or becomes more expensive, the economy is likely to
su¤er a drop in GDP growth. This might put additional pressure on the …scal
position through its impact on tax revenues, likely to be lower as activity falls. 10
Relatedly, Laeven and Valencia (2011) focus on the impact of …nancial sector
interventions on the capacity of the …nancial system to provide credit. Their
results show that …rms dependent on external …nancing bene…ted signi…cantly
from bank recapitalization operations. However, as documented in Acharya, if
the sovereign becomes overburdened, the value of the public guarantees falls,
deepening the interconnection of stress. Kollmann et al. (2012) also focus
on the impact of bank rescues. Their message is positive and highlights the
ability of bank rescue operations to improve macroeconomic performance. Still,
while they show that bank rescues raise investment, in line with the evidence in
Broner et al. (2014) or Popov and Van Horen (2013), they …nd that sovereign
debt purchases by domestic banks lead to a crowding out of private investment.
Gray and Jobst (2011) and Gray et al. (2013) present a less benign exercise
showing the potentially high impact on …scal risk associated to the existence of
Finally, if uncertainty augments the crisis could lead to a sudden stop of
capital in‡ows. In this line, Reinhart and Rogo¤ (2008) argue that banking
crises often follow credit booms and high capital in‡ows. Moreover, they …nd
that periods of high international capital mobility gave rise to banking crises
in the past. Cavallo and Izquierdo (2009) provide further evidence showing
that, after …nancial crises in emerging markets, capital ‡ows may collapse for
months or years potentially triggering a solvency crisis. Indeed, as argued by
Obstfeld (2011) when discussing the role of international liquidity in the recent
debt crisis, “. . . gross liabilities, especially those short-term, are what matter”.
Van Rixtel and Gasperini (2013) show that sovereign risk, as measured by the
sovereign swap spreads, has shown in some periods a strong correlation with the
three-month USD Libor-OIS, a sign that borrowing strains in foreign currency
for banks a¤ect the creditworthiness of the sovereigns.
In turn, a number of transmission channels of a …scal crisis on the broader
economy can be traced through the domestic …nancial system. 11 Whenever
assets need to be written o¤ or rescheduled, domestic banks are usually the
…rst in line to take a hit. Along these lines, Noyer (2010), argues that banks’
holdings of defaulted government bonds might lead to large capital losses and
threaten the solvency of elements of the banking sector. IMF (2002) provides a
comprehensive overview of the e¤ects of four sovereign restructurings (Ecuador,
Pakistan, Russia and Ukraine) on the domestic banking sector. The paper documents
the extent of direct losses from banks’holdings of government securities,
an increase in the interest rates on liabilities not matched by increased returns
on assets (on the contrary, in this context government securities usually o¤er
non-market rates), and an increase in the rate of non-performing loans increases,
as higher …nancing costs lead to corporate bankruptcies. Similarly, Erce (2012)
10 See De Paoli et al. (2009) or Feenstra and Taylor (2008).
11 See IMF (2002) or Reinhart and Rogo¤ (2012).
suggests that the degree of bank intermediation and the banking system exposure
to the sovereign strongly in‡uence a debt crisis ripple e¤ect on the real
economy. In addition, authorities often react to debt problems by coercing domestic
creditors to hold government bonds in non-market terms (Diaz-Cassou
et al., 2008). 12 While this keeps borrowing costs low, a government default may
trigger a banking crisis. 13 In Darraq-Pires et al. (2013) the positive connection
between sovereign and bank risk is due to banks investing in government
securities to hedge future liquidity shocks. Along these lines, Angeloni and
Wol¤ (2012) assess the impact of sovereign bond holdings on the performance
of banks during the euro area crisis using individual bank data and sovereign
bond holdings. They …nd that peripheral sovereign bonds a¤ect banks’stock
market valuations heterogeneously. While Italian, Irish and Greek debt appear
to have negatively a¤ected the market valuation of the banks holding them,
such an e¤ect is not signi…cant for other peripheral sovereign debt, most notably,
Spanish. 14 Acharya et al. (2013), document the high exposure of their
sample banks to their own sovereign, which according to their theory should be
a main channel through which stress feeds back. 15
Beyond this direct balance sheet e¤ect, the ensuing …scal contraction may
lead to reduced activity, a¤ecting banks’pro…ts and further damaging the …nancial
system. Moreover, a credit crunch may worsen the economic downturn, as
banks reduce lending due to capital losses and to the increase in uncertainty that
comes with a sovereign debt default (Panizza and Borenzstein, 2008). Popov
and Van Horen (2013) focus on the feedback from sovereign risk into banking
risk by assessing the extent to which increasing holdings of distressed sovereign
bonds limit the banks’ability to extend loans to the private sector, furthering
the vicious feedback loop by limiting the growth potential of the economy. They
document a stronger reallocation away from domestic lending in the periphery.
A similar crowding out e¤ect is present in Broner et al. (2014), who present a
battery of stylized facts for the euro area, including both an increase in sovereign
bond holdings by banks and a simultaneous drop in …nancing to the private sector.
16 Corporate borrowers and banks may face a sudden stop after a sovereign
default even if their exposure to government bonds is limited. Gennaioli et al.
(2010) and Erce (2012) argue that sovereign defaults trigger capital out‡ows
and credit crunches. An additional pressure to curtail lending might come from
12 Das et al. (2012) argue that regulatory factors could lead to further balance sheet intertwining.
In Livshits and Schoors (2009), as public debt becomes risky, governments have
incentives to not adjust prudential regulation.
13 In past crises, prudential regulation treated government bonds as risk-free despite default
expectations were not zero (IMF, 2002). According to Castro and Mencia (2015), a similar
phenomenon has been at play in the Eurozone
14 A caveat of this analysis is that data stops in mid-2012, before the height of stress in Italy
15 Among other things, the paper assesses the extent to which reduced sovereign ratings
a¤ected the banks CDS through their e¤ect on the public guarantees.
16 These papers present a nuanced view of domestic purchases of public debt. Others have
found positive e¤ects. According to Asonuma et al. (2015) and Andritzky (2012), domestic
bank purchases of sovereign bonds help stabilize sovereign funding costs.
the fact that the economic uncertainty may lead to deposit runs or a collapse
of the inter-bank market (Panizza and Borenzstein, 2008). Finally, sovereign
rating downgrades further limit banks’ access to foreign …nancing, leading to
sudden stops or higher borrowing costs (Reinhart and Rogo¤, 2012).
On the sovereign front, some authors have measured credit risk using credit
ratings (Correa et al., 2012) or bond spreads (Mody and Sandry, 2011). In turn,
bank risk proxies previously used include credit ratings (Correa et al., 2012) and
the stock market behavior (Angeloni and Wol¤, 2012). The analysis here follows
a recent strand of the literature that has opted for using credit default swaps
(CDS). By design, CDS contracts shield the holders from events of default, so
are the …nancial instruments most related to credit risk. Importantly, although
the data spans back a little less than a decade, CDS markets are relatively
liquid. 17 Monthly data for 5-year CDS contracts for both individual banks
and sovereigns comes from Bloomberg and DataStream. For sovereign CDS
data, in most countries the information spans back to late 2005. In order to be
able to assess the various twists observed during the crisis, countries for which
sovereign CDS data was missing prior to 2008 (Cyprus and Luxembourg) were
excluded from the sample. In turn, the above-cited sources returned active
CDS contracts for 48 banks in the euro area. Unfortunately, prior to 2007,
the coverage was less homogeneous. When considering together the coverage of
both banks and sovereign entities, su¢ ciently large series were available for 10
euro area countries: Germany, Italy, France, Spain, Ireland, Greece, Portugal,
Belgium, Netherlands and Austria. 18
As in Acharya et al. (2013), to have a system-wide measure of bank stress,
individual bank CDS data is aggregated in a country-speci…c bank risk index.
De…ning the CDS of bank j 2 J from country i at time t by Bank CDS jit and
the corresponding weight as w jit ,country’s i Bank Risk Index is
BankRisk it = X 8j2J
w jit BankCDS jit
From the various weighting schemes available, for simplicity, this paper uses
w jit = 1 J :19
The econometric exercise controls for various macroeconomic, …nancial and
global factors. Data on sovereign ratings comes from Fitch. Data on the banks’
balance sheets come from Haver Analytics, the European Central Bank, the
17 An important limitation of CDS data relates to the existence of counterparty risk. The
lack of detailed data on CDS counterparties prevents from controlling for this potential bias.
18 There is no CDS data for Finnish banks, preventing its inclusion in the analysis.
19 Banks weights could be set according to their market capitalization or total assets. While
the …rst option above focuses on private capital, depending on the extent of bank nationalisation,
the second can be more adequate.
Bank for International Settlements and the IMF’s Financial Stability Indicators.
20 The series included are: total assets, exposure to the general government,
funding from the central bank, foreign assets and liabilities, non-performing
loans, return on assets and equity ratio. Macroeconomic data (unemployment,
in‡ation, nominal GDP growth, …scal de…cit, current account and public debt)
was obtained from Haver Analytics. 21 The Itraxx …nancial Junior and VIX
index come from Bloomberg.
3 Preliminary Evidence
Figures 1 and 2 (in the Appendix) provide a bird’s eye view on the behavior of
the risk series. Figure 1 portrays the behavior of sovereign and bank risk from an
aggregate perspective. Euro area wide sovereign stress is proxied using a simple
average of sample countries’sovereign CDS. The Itraxx Junior represents bank
risk. In turn, Figure 2, shows the behavior of sovereign and bank on a countryby
As a reminder of the importance of policy action, the shadowed areas in
Figure 1 represents two periods of marked policy activism. The …rst depicts
the two months of 2008 in which most sample countries enacted programs of
support for their …nancial systems. Remarkably, even at the low frequency
employed here, the very speci…c dynamics ongoing during the third quarter of
2008 are still apparent. On the back of the public guarantees, the bank credit
risk decreased markedly. However, simultaneously, the sovereign CDS started
to pick up. According to Acharya et al. (2013), the increasing sovereign CDS
re‡ected market fears regarding the just absorbed liabilities. The second period
shadowed in Figure 1 corresponds to that following the ECB announcement of
the Outright Monetary Transactions (OMT) instrument (August 2012). While
it is not apparent that such policy action changed the correlation, Figure 1
shows a change in risk dynamics. Since then, both risk indicators have trended
down. Another way to look at time patterns for the correlation between the risk
variables comes from comparing sub-periods. This is done in Table 1 below.
20 IMF’s FSI indicators (non-performing loans, return on assets and equity ratio) are available
only since 2008.
21 Converse to the literature on sovereign spreads that focuses on real GDP, nominal GDP
is used given its relevance in markets’assessment of debt sustainability. The debt and …scal
data refers to the General Government. These variables, as GDP, are available only on a
quarterly basis. They have been linearly interpolated into monthly frequency.
In periods 2 and 3 (bail-out and …scal activism), the correlation observed
previously broke down. Remarkably, since the inception of the OMT, the correlation
is back to its pre-crisis value. 22 Following, Broner et al. (2014) narrative
of the crisis, further insights into the dynamic relation of the risk indicators can
be gained by breaking the euro area into a core and a periphery. This is done
by running the following regressions
Risk_A it = i + X
Risk_A it = i +
p P eriod p dummy Risk_Z it 1 + " it ; (1)
r region r dummy Risk_Z it 1 + it (2)
where Risk_A it and Risk_Z it stand, interchangeably, for country’s i sovereign
and bank risk. Within regression (1) the feedback e¤ect from one risk
to the other is allowed to depend on the speci…c periods described in Table
1. In turn, within regression (2) the coe¢ cients are allowed to di¤er di¤er between
core and peripheral countries. The results are presented in Table 2 in the
Appendix. The European crisis period (January 2010-August 2012) featured
a particularly large degree of pass-through from bank risk into sovereign risk.
Notably, feedback loops are not too di¤erent in peripheral and core countries.
If anything, bank risk has a stronger pass-through e¤ect on sovereign risk in peripheral
economies. Overall, there is some evidence of the correlation between
risk indicators having diverged across time and regions. The rest of the paper
attempts to connect this time and spatial variation in risk to the dynamics of
the underlying macroeconomic conditions.
4 Econometric Analysis
This section presents a panel data model of the feedback loop for each risk
variable. 23 As in Thukral (2013) or Heinz and Sun (2014), the starting point is
22 To complement the data description, Table A1 in the Appendix presents summary statistics
for the full sample and for the core and periphery subsamples.
23 The low number of observations calls for pooling country data to take advantage of both
time series and cross-country variation and for keeping the model as parsimonious as possible.
a Generalized Least Squares (GLS) estimator, using the CDS variables in levels.
Following the literature, in addition to the risk indicators, the model controls
for …nancial, global, macroeconomic, and contagion e¤ects:
Risk_A it = Ai + ZA Risk_Z it 1 + AA X A it 1 + ZA X Z it 1 + GA X G it 1 + " A it
Within this framework, the coe¢ cient ZA measures the extent to which
Risk Z feeds into Risk A. In addition, the model controls for the primary
determinants of Risk A (Xit A) and Risk Z (XZ it ). When dealing with the sovereign
risk model, Xit A collects the macro variables and XZ it collects the banking
sector variables. When dealing with the bank risk model, this reverses. The
variable Ai collects country-speci…c characteristics. Euro area sovereign debt
markets have been subject to recurrent bouts of dramatic co-movement during
the crisis, which a number of commentators have associated with contagion. 24
This cross-sectional correlation can bias the standard errors, making the estimations
less reliable. To address this issue the model controls for global and
contagion factors (Xit G ). To gauge the relative importance role of the di¤erent
sets of covariates, they are included and discussed in steps.
Additionally, the high degree of persistence of the CDS series raises concerns
about the robustness of the results. To address this concern the model
incorporates dynamic e¤ects by including a lag of the dependent variable,
(1 A L)Risk_A it = Ai + ZA Risk_Z it 1 + X it 1 + " A it
where L is the lag operator, A is the autoregressive coe¢ cient of Risk A,
= [ AA; ZA; GA] and X it 1 = [Xit A 1 ; XZ it 1 ; XG it 1 ]. The bias (Nickel bias)
introduced by the dynamic element is tackled by using system-GMM (Arellano
and Bover, 1995), which relies on the use of internal instruments (lagged levels
and di¤erences of the endogenous and predetermined variables).
4.1 Sovereign Risk Model
In a …rst step, similar to D’Agostino and Ehrmann (2014), the model only uses
the macro factors. The variables included are: debt to GDP, …scal balance,
…nancial account, GDP growth, unemployment and in‡ation. 25 The results
(Column 1, Table 3) are broadly in line with previous literature. Remarkably,
the …scal balance shows no signi…cant relation with sovereign risk. Next, to
assess the importance of banking factors for the pricing of sovereign risk, the
model also includes the bank risk determinants. Following the literature, the regressors
include: loan quality (non-performing loans to total loans), pro…tability
Signi…cant gaps in Greek data preclude its use on the econometric part.
24 According to Alter and Beyer (2013) or Broto and Perez-Quiros (2013) contagion played a
non-negligible role in peripheral countries. Heinz and Sun (2014) …nd that shocks to Spanish
and Italian CDS delivered the largest spillovers.
25 The Breusch-Pagan Lagrange Multiplier test strongly supported the inclusion of random
(return on assets), bank capital (tangible common equity ratio), the home bias
in the banks’portfolio (domestic assets as a % of total assets), the exposure to
public entities (private assets over total assets) and a measure of funding stability
(assets to deposits). The results, in column 2, serve as test for the …nancial
dominance hypothesis (Thuckar, 2013). While banking variables heavily in‡uence
the behavior of sovereign risk, converse to Thuckar (2013), macroeconomic
factors play a dominant role. 26
The next step adds BankRisk it to the framework. The coe¢ cient associated
with the bank risk indicator measures the feedback from bank into sovereign risk.
Column 3 presents the results for this model. There is a positive and signi…cant
relation between bank and sovereign risk. For every 10 basis points (bps) increase
in bank risk, sovereign risk increases by 4.2 bps in the following month.
This is a large degree of pass-through. To lower the degree of commonality in the
error terms, the model also controls for global shocks and potential contagion
e¤ects. 27 To proxy contagion, the model includes the average of the sovereign
CDS for other euro area countries. In turn, the model includes the VIX index to
proxy for global shocks. Column 4 from Table 3 presents the results. While the
VIX Index does not appear to have a signi…cant relation to sovereign risk, the
contagion indicator presents a highly signi…cant positive relation with sovereign
risk. Controlling for global and contagion e¤ects does not alter the signi…cance
of pass-through, although the size of the coe¢ cient becomes smaller (3.1 bps
increase in sovereign risk for every 10 bps increase on bank risk). 28
Finally, column 5 presents a dynamic version of the sovereign risk model.
As detailed above, the model is estimated using system GMM. 29 The dynamic
element is large (close to unity) and highly signi…cant. Remarkably, while the
pass-through from bank to sovereign risk remains signi…cant, the sign reverses.
According to the results, for every 10 bps increase in bank risk, sovereign risk
decreases by 0.9 bps.
4.2 Bank Risk Model
Following similar steps, the bank-related variables are included …rst. Next, the
macroeconomic controls are introduced. Global shocks are again proxied with
the VIX. Instead, contagion e¤ects are now accounted for using the Itraxx Junior
index. Finally, the dynamic version of the model, including the lagged value of
bank risk, is estimated. Table 4 presents the results for these models.
As shown in Columns 1 and 2 of Table 4, banks with a larger home bias
and larger private sector credit face larger bank risk. Non-performing loans are
associated, as expected, with higher bank risk. Interestingly, a lower ratio of
26 The regression’s R-squared increases by more than 50% after adding the bank variables,
but still gives macro factors a larger weight in explaining the sovereign risk variance.
27 A Pesaran test on the model´s residuals shows a signi…cant degree of spatial correlation.
28 The results (available under request) using a two-step Driscoll-Kraay correction for crosssectional
correlation are almost undistinguishable.
29 Both the Sargan endogeneity tests and the Di¤erence-in-Hansen tests of exogeneity tests
validate the instruments.
assets to deposits and higher bank capital are associated with larger levels of
stress. This result could be re‡ecting the fact that banks located in countries
with stronger sovereigns have less need to build their own capital cushions (as
in De Grauwe and Ji, 2013). 30 Column 3 shows the results for the model
including the lagged value of sovereign risk. The feedback coe¢ cient is, again,
highly signi…cant (0.53). In turn, as expected, larger values for the Itraxx
and VIX Indices associate with more bank risk (column 4). Contagion across
banks is a signi…cant phenomenon. Finally, column 5 of Table 4 presents the
estimates for the dynamic model of bank risk. The coe¢ cient of main interest,
the one associated with the sovereign risk indicator, is positive and signi…cant.
According to the results, a 10 bps increase in sovereign risk leads to a 0.8 bps
increase in bank risk.
4.3 A cheat impulse-response
Combining the pass-through coe¢ cients obtained from the sovereign and bank
risk models, one can recoup the dynamic response of sovereign and bank risk
to shocks to one another. The …gures below present a graphical representation
of shocking such system of equations with a 50 bps shock to sovereign risk (left
chart) and to bank risk (right chart).
Figures 3.1 and 3.2 illustrate the di¤erent form that average feedback e¤ects
take. On the one hand, there is a strong positive feedback arising from sovereign
shocks (Figure 3.1). On the other, there is no evidence of a feedback loop from
bank risk into sovereign risk. Quite the opposite, bank risk shocks induce a
milder and negative reaction of sovereign risk (Figure 3.2).
30 In unreported estimates using the Driscoll-Kraay correction, the results are qualitatively
5 Digging into the Sources of Feedback Loops
The relation between both risks might depend on the underlying economic and
…nancial environment. For instance, according to Acharya et al. (2013) or Martin
et al. (2014), explicit and implicit balance sheet interrelations can powerfully
amplify feedback loops. This section tests what conditions a¤ect the intensity
of the pass through by incorporating interactions between the risk measure and
(1 A L)Risk_A it = Ai + ZA Risk_Z it 1 + F ZA F it 1 Risk_Z it 1 + X it 1 +" A it
where F it 1 is the factor interacting with the Risk Z. Within this framework,
the feedback between risks becomes:
@Risk_Z it 1
= ZA + F ZA F it 1
The sovereign risk model with interactions is estimated for the following
variables: size of the banking system (Gennaioli al., 2014), banks’foreign liabilities
(Cavallo and Izquierdo, 2009) and banks’non-performing loans (Acharya
et al., 2013). 31 In turn, the candidate variables for a¤ecting the feedback from
the sovereign to the banks are public debt to GDP (Mody and Sandry, 2011),
banks’balance sheet exposure to the sovereign (Angeloni and Wol¤, 2012), and
the investment grade status of sovereign debt (Correa et al., 2012). Table 5
(sovereign risk) and Table 6 (bank risk) contain the result.
Table 5 vindicates the validity of most of the above-mentioned channels
of transmission. It shows that the three interactions present signi…cant positive
spillovers from bank to sovereign risk. The pass-through of risk becomes
stronger where the volume of non-performing loans and banks’foreign liabilities
are larger. Conversely, there is no evidence that, where banks have bigger
balance sheets, the feedback e¤ect is stronger.
In turn, Table 6 shows that the feedback from sovereign into bank risk is
stronger the larger the stock of public debt and larger banking system exposure
to the sovereign. The results also show a signi…cantly stronger pass-through of
sovereign risk when the sovereign rating is below investment grade. 32 When a
sovereign rating falls outside the investment grade category, it loses a large pool
of potential investors, a¤ecting negatively sovereign risk.
5.1 Economic signi…cance
To grasp the economic relevance of these results, Figures 4.1 and 4.2 depict
various e¤ects in basis points (bps). Figure 4.1 shows how the pass-through onto
31 All the variables are measured as percentage of GDP to make them relative to the authorities’potential.
32 This is despite the fact that the adjustments to the ECB’s collateral policy during the
crisis (Eberl and Webber, 2014) ameliorated the impact of not having an investment grade.
sovereign risk of a 100 bps increase in bank risk depends on di¤erent values of
F it . Figure 4.2 does the same for the e¤ect on bank risk of a 100 bps increase in
sovereign risk. The …gures compare the e¤ects at the minimum and maximum
values within sample of the corresponding indicators.
Some of the conditional risk dynamics are economically very sizeable. For
instance, Figure 4.1 shows that a 100 bps increase in bank risk does not lead
to a positive feedback on sovereign risk even if the banking system size is at
its maximum within the sample. The feedback is, instead, very large when the
asset quality of the banks, as measured by the share of non-performing loans
(NPLs), is high. While for the lowest level of NPLs there is no positive feedback
e¤ect, at the maximum value within sample, the e¤ect is well above 150 bps.
Similarly, when banks’foreign liabilities are large, there is a sizeable positive
feedback e¤ect of bank risk to sovereign risk. In turn, Figure 4.2 shows the
relevance of the balance sheet exposure to the sovereign in the transmission
of stress. Faced with an increase in sovereign risk of 100 bps, banking systems
holding the lowest level of exposure face an 18 bps increase in their risk. Instead,
banks with larger exposures face an increase of 80 bps. The feedback e¤ect can
also grow considerably in the presence of large public debt stock (up to 62 bps),
and when the sovereign has lost its investment grade (40 bps).
6 Bank Rescues and the Feedback Loop
This section uses the sovereign risk model to assess quantitatively the e¤ect
that bank rescue operations can have on the feedback from bank into sovereign
risk. According to Acharya et al. (2013), the rescue packages enacted by euro
area governments to …ght o¤ the …nancial crisis generated a risk transfer. As
sovereigns began to support their banks, investors became more con…dent about
banks. This led to a lowering of banks’CDS spreads. Unfortunately, in some
cases, the weight governments had to lift pushed up sovereign risk, facilitating
the emergence of a perverse feedback loop. 33 To limit extreme forms of this risk
transfer, the euro area authorities devised a tool to assist banks directly using
the European Stability Mechanism (ESM, 2014). 34 Implementing this policy
requires determining when a sovereign might not be able to do it on its own.
The analysis focuses on direct exposures and contingent liabilities. 35
Figure 5 provides a dynamic representation of the e¤ects of a shock to bank
risk when the sovereign has bailed out the banks using an amount equal to the
average …scal cost of bank crises (15% of GDP) in Laeven and Valencia (2011).
In line with Acharya et al. (2013) risk-transfer hypothesis, the results, presented
in Table 7, point to a signi…cantly larger pass-through of bank risk into
the sovereign for those economies where the authorities more heavily supported
their banking system. According to the results, given a size of the bailout equal
to 15% of GDP, for every 100 bps increase in bank risk, sovereign risk increases
by 11 bps within a year. As shown in columns 3 and 4, this e¤ect becomes more
sizeable for countries where the banks have a larger amount of foreign liabilities
or a larger balance sheet exposure to the sovereign.
7 Conclusions and Policy implications
This paper has analyzed the factors associated with the emergence of perverse
spirals of sovereign and bank stress. Using a dynamic panel data model, it
uncovers underlying vulnerabilities that reinforce the process where shocks to
33 Alter and Beyer (2013) …nd that, in Spain, the nationalization of Bankia led to an increase
34 Direct recapitalisation is provided if a sovereign cannot provide support without triggering
a …scal crisis.
35 The data, in an annual format, comes from the European Commission.
a country´s …scal health contaminate the …nancial sector. Countries where
public debt is larger, and where domestic banks have a larger exposure to their
own sovereign, face stronger feedback loops from sovereign into bank risk. The
same goes for countries losing their investment grade status. On the other, the
analysis also identi…ed factors associated with an elevated transmission of bank
distress to the sovereign. In countries where banks are larger, funded with more
foreign credit and face more non-performing loans, the feedback from bank risk
into sovereign risk is stronger.
From an economic policy perspective, these results can help in monitoring
the build-up of …scal weaknesses and the robustness of the …nancial system to
…scal shocks. Additionally, the new framework to handle banking crises in the
euro area implies that, if the foreseen bail-in of the bank’s private creditors is
not enough, individual banks could be rescued directly by the o¢ cial sector.
For such direct recapitalization to happen, it has to be the case that the country
could endanger its sustainability if supporting the bank alone. This paper
informs this process by studying the circumstances in which …nancial rescues
might overburden the sovereign.
 Acharya, V., I. Dreschlet, and P. Schnabl (2013), A Pyrrhic Victory: Bank
Bailouts and Sovereign Credit Risk, Mimeo.
 Acharya, V., and B. Tuckman (2013), Unintended Consequences of LOLR
Facilities: The Case of Illiquid Leverage, NBER Working Paper 19773.
 D’Agostino, A., and M. Ehrmann (2013), The pricing of G7 Sovereign bond
spreads: The times, they are a-changing, Journal of Banking and Finance,
 Alessandri, P. and A. Haldane (2009), Banking on the State, Bank of England,
 Alter, A. and A. Beyer (2013), The Dynamics of Spillover E¤ects during
the European Sovereign Debt Turmoil, Mimeo.
 Andritzky, J. (2012), Government Bonds and Their Investors: What are
the Facts and Do They Matter?, IMF Working Paper 12/158.
 Angeloni, C., and G. Wol¤, 2012, Are Banks A¤ected by Their Holdings
of Government Debt?, Bruegel Working Paper 2012/07.
 Arce, O., Mayordomo, S., and J. Pena (2012), Credit-risk valuation in
the sovereign CDS and bond markets: Evidence from the euro area crisis,
CNMV Working Paper Series, No. 53.
 Arellano, C. and N. Kocherlakota (2008), Internal Debt Crises and Sovereign
Defaults, NBER Working Paper 13794.
 Asonuma, T., Bakhache, S., and H. Hesse (2015), Is Banks’ Home Bias
Good or Bad for Public Debt Sustainability?, IMF Working Paper 15/44.
 Baldacci, E. and S. Gupta (2009a), Fiscal Expansions: What Works, Finance
& Development, Volume 46, Number 4.
 Baldacci, E., C. Mulas-Granados and S. Gupta (2009b), How E¤ective is
Fiscal Policy Response in Systemic Banking Crises?, IMF Working Papers
 Balteanu, I., and A. Erce, 2014, Bank Crises and Sovereign Defaults in
Emerging Markets: Exploring the Links. Bank of Spain, Working Paper
 Broner, F., Erce, A., Martin, A., and J. Ventura (2014), Sovereign Debt
Markets in Turbulent Times: Creditor Discrimination and Crowding Out,
Journal of Monetary Economics, Volume 61.
 Broto, C. and G. Perez Quiros (2013), Disentangling contagion among Sovereign
CDS spreads during the European debt crisis, Bank of Spain Working
Paper Series No. 1314.
 Candelon, B. and F.C. Palm (2010), Banking and Debt Crises in Europe,
The Dangerous Liaisons?, CESifo Working Paper No. 3001.
 Caprio, G. Jr. and P. Honohan (2008), Banking Crises, Institute for International
Integration Studies Discussion Paper Series no. 242.
 Castro, C., and J. Mencia (2014), Sovereign Risk and Financial Stability,
Revista de Estabilidad Financiera No. 26, Bank of Spain.
 Cavallo, E. and A. Izquierdo (2009), Dealing with an International Credit
Crunch: Policy Responses to Sudden Stops in Latin America, Inter-
American Development Bank, mimeo.
 Carey, D. (2009), Iceland: The Financial and Economic Crisis, OECD
Economics Department Working Papers, No. 725, OECD Publishing.
 Correa, R., Kuan-Hui, L., Sapriza, H., and G. Suarez (2012), Sovereign
Credit Risk, Banks’Government Support, and Bank Stock Returns Around
the World, Federal Reserve Board, International Finance Discussion papers
 Das, U.S., Oliva, A.O., and T. Tsuda (2012), Sovereign Risk: A Macro-
Financial Perspective, Public Policy Review Vo. 8, No. 3 (Ministry of Finance,
 Delatte, A. L., Fouquau, J. and R. Portes (2014), Nonlinearities in Sovereign
Risk Pricing: The Role of CDS Index Contracts. OFCE Working
 De Grauwe, P., and Y. Ji (2013), Strong Governments, Weak Banks, CEPS
Policy Brief No. 305.
 De Paoli, B., Hoggarth, G. and V. Saporta (2009), Output costs of sovereign
crises: some empirical estimates, Bank of England working papers no. 362.
 Diaz-Cassou, J., A. Erce and J. Vazquez (2008), Recent episodes of sovereign
debt restructuring: A case-study approach, Bank of Spain, Occasional
 Diebold, F., and K. Yilmaz (2009), Measuring …nancial asset return and
volatility spill overs, with an application to global equity markets, Economic
Journal, Issue 119.
 Dreschler, I., Drechsel, T., Marques-Ibanez, D., and P. Schnabl (2013),
Who Borrow from the Lender of Last Resort?, Mimeo.
 Eberl, J., and C. Webber (2014) ECB Collateral Criteria: A Narrative
Database 2001-2013, Ifo Working Paper No. 174
 Erce, A. (2012), Selective Sovereign Defaults, Federal Reserve Bank of Dallas,
Globalization and Monetary Policy Institute, Working Paper No. 127.
 European Commission (2009), Public Finance Report in EMU-2009, European
Economy No. 5.
 European Stability Mechanism (2014), ESM direct bank recapitalisation
instrument adopted, Press release no 18/2014
 Feenstra, R.C and A. Taylor (2008), International Economics, Worth Publishers.
 Gennaioli, N., Martin, A. and S. Rossi (2014), Sovereign Default, Domestic
Banks and Financial Institution, Journal of Finance, forthcoming.
 Gray, D. and A. Jobst (2011), Modelling systemic …nancial sector and sovereign
risk, Sveriges Riskbank Economic Review, 2011:2.
 Gray, D., Gross, M., Paredes, J., and M. Sydow (2013), Modelling Banking,
Sovereign and Macro Risk in a CCA Global VAR, IMF Working Paper
 Heinz, F. and Y. Sun (2014), Sovereign CDS Spreads in Europe: The
Role of Global Risk Aversion, Economic Fundamentals, Liquidity, and Spill
overs, IMF Working Paper 14/17.
 Honohan, P. (2008), Risk Management and the Costs of the Banking Crisis,
The Institute for International Integration Studies Discussion Paper Series
 IMF (2002), Sovereign Debt Restructurings and the Domestic Economy
Experience in Four Recent Cases, Policy Development and Review Department.
 King, M. (2009), Time to buy or just buying time? The market reaction
to bank rescue packages, BIS Working Paper No. 288.
 Kollmann, R., Roeger, W., and J. in’t Veld (2012), Fiscal Policy in a Financial
Crisis: Standard Policy vs. Bank Rescue Measures, Ecares Working
 Laeven, L. and F. Valencia (2011), The Real E¤ects of Financial Sector
Interventions During Crises, IMF Working Paper 11/45.
 Livshits, I. and K. Schoors (2009), Sovereign Default and Banking, mimeo.
 Mody, A., and D. Sandri, 2011, The Eurozone Crisis: How Banks Came to
be Joined at the Hip, IMF Working Paper 11/269.
 Moody’s (2014), European Sovereign Debt and Banking Crises: Contagion,
Spill overs and Causality, Moody’s Investors Service, Credit policy.
 Noyer, C (2010), Sovereign crisis, risk contagion and the response of the
central bank, mimeo.
 Panizza, U and E. Borensztein (2008), The Costs of Sovereign Default, IMF
Working Paper 08/238.
 Reinhart, C.M. and K.S. Rogo¤ (2008), Banking Crises: An Equal Opportunity
Menace, NBER Working Papers 14587.
 Reinhart, C.M. and K.S. Rogo¤ (2012), From Financial Crash to Debt
Crisis, American Economic Review, 101(5).
 Rosas, G. (2006), Bagehot or Bailout? An Analysis of Government Responses
to Banking Crises, American Journal of Political Science Vol. 50
No.1, pages 175-191.
 Popov, A., and N. Van Horen (2013), The impact of sovereign debt exposure
on bank lending: Evidence from the European debt crisis, DNB Working
Paper No. 382.
 Thukral, M. (2013), Bank dominance: Financial sector determinants of
sovereign risk premia, Mimeo.
 Van Rixtel, A., and G. Gasperini (2013), Financial Crises and bank funding:
recent experience in the euro area, BIS Working Papers No. 406.
Data Series Source Frequency
Local currency rating Fitch Monthly
Harmonized CPI Index Haver Analytics Monthly
Nominal GDP Haver Analytics Quarterly
Financial Account Balance Haver Analytics Quarterly
Harmonized Unemployment Rate Haver Analytics Monthly
Variables included in the analysis: Main features
General Government: Net
Banking System Balance Sheet Haver Analytics Monthly
VIX index CBOE Monthly
Itraxx Junior Financial Indices Bloomberg Monthly
Central Bank Lending Individual Central Banks Monthly
Bank rescue operations (liabilities
and contingent liabilities)
Sovereign 5-year CDS spreads Bloomberg and Datastream Monthly
Bank 5-year CDS spreads Bloomberg and Datastream Monthly
Figure 1: Sovereign and Bank Risk in the Euro Area
1000 1500 2000
1000 1500 2000
100 200 300 400
100 200 300 400
Figure 2: A bird’s eye view of Sovereign and Bank risk
Austria Belgium France
2005m1 2010m1 2015m1
2005m1 2010m1 2015m1 2005m1 2010m1 2015m1
5-YEAR CDS SOVEREIGN
5-YEAR CDS BANK INDEX
2005m9 2014m1 2005m9 2014m1
5-YEAR CDS SOVEREIGN
5-YEAR CDS BANK INDEX
Variable Observations Mean Std. Dev. Min Max Observations Mean Std. Dev. Min Max Observations Mean Std. Dev. Min Max
Sovereign CDS 497 55.18 59.88 1.30 329.28 450 263.28 525.55 1.76 6882.40 947 154.07 379.19 1.30 6882.40
Bank CDS Index 505 134.83 87.57 7.93 431.49 471 399.27 436.90 8.10 2067.82 976 262.45 336.83 7.93 2067.82
Public Debt (% GDP) 485 86.23 17.64 50.21 118.93 485 94.61 35.58 25.62 183.29 970 90.42 28.38 25.62 183.29
GDP Growth 470 0.65 0.68 -1.52 1.56 470 0.17 1.19 -2.87 2.86 940 0.41 1.00 -2.87 2.86
Fiscal Balance (% GDP) 467 -2.70 3.88 -13.85 7.52 485 -6.90 7.12 -40.31 8.69 952 -4.84 6.13 -40.31 8.69
Inflation 500 1.99 1.05 -1.64 5.77 500 2.07 1.64 -2.92 5.68 1000 2.03 1.38 -2.92 5.77
Unemployment 500 6.71 2.21 3.00 11.30 498 12.28 5.91 4.20 27.80 998 9.49 5.25 3.00 27.80
Financial account (% GDP) 485 -2.96 3.97 -10.24 5.28 485 5.31 4.64 -6.75 13.80 970 1.17 5.98 -10.24 13.80
Central Bank Liquidity (% GDP) 485 0.07 0.04 0.01 0.37 485 0.20 0.21 0.00 0.86 970 0.13 0.16 0.00 0.86
Bank Size (% GDP) 485 4.09 0.48 3.20 5.05 485 5.04 2.96 2.03 12.95 970 4.56 2.17 2.03 12.95
Bank access to Central Bank
(% of total assets)
Bank exposure to General
Government (% total assets)
Bank foreign liabilities (%
Table A1. Summary statistics by geographical area: Core versus periphery
Core Periphery Full Sample
495 0.02 0.01 0.00 0.08 495 0.04 0.05 0.00 0.24 990 0.03 0.04 0.00 0.24
495 0.07 0.02 0.04 0.14 495 0.06 0.03 0.02 0.12 990 0.06 0.02 0.02 0.14
495 0.14 0.10 0.04 0.42 495 0.12 0.13 0.02 0.44 990 0.13 0.12 0.02 0.44
Bank Home Bias 495 0.76 0.12 0.44 0.87 495 0.83 0.19 0.42 0.96 990 0.79 0.16 0.42 0.96
Non-performing loans 315 2.95 0.93 0.51 4.37 321 8.91 6.08 0.75 29.37 636 5.96 5.29 0.51 29.37
Return On Assets 291 0.27 0.30 -1.31 0.74 312 0.16 1.51 -9.52 8.11 603 0.21 1.10 -9.52 8.11
Capital ratio 291 14.43 2.40 10.47 19.64 321 11.73 3.09 -2.89 20.29 612 13.01 3.09 -2.89 20.29
VIX Index 505 21.47 10.13 10.31 68.51 505 21.47 10.13 10.31 68.51 1010 21.47 10.13 10.31 68.51
Itraxx Junior 505 189.70 134.92 12.70 529.63 505 189.70 134.92 12.70 529.63 1010 189.70 134.85 12.70 529.63
Data runs from September 2007 until January 2014. Core countries are Germany, France, Belgium, Austria and Netherlands. Periphery countries include Ireland, Italy, Portugal, Greece and Spain.
Table 2. Bank and Sovereign risk loops by periods and regions
Dep. Var: Sovereign Risk Full Sample Core vs Periphery
Bank Risk Index (during Period 1)
Bank Risk Index (during Period 2)
Bank Risk Index (during Period 3)
Bank Risk Index (during Period 4)
Bank Risk Index (during Period 5)
Bank Risk Index (if core country)
Bank Risk Index (if peripheral country)
Constant 2.77e+01** 6.64
Observations 890 890
R-squared 0.57 0.47
Dep. Var: Bank Risk
Core vs Periphery
Sovereign Risk (during Period 1)
Sovereign Risk (during Period 2)
Sovereign Risk (during Period 3)
Sovereign Risk (during Period 4)
Sovereign Risk (during Period 5)
Sovereign Risk (if core country)
Sovereign Risk (if peripheral country)
Constant 7.46e+01*** 9.93e+01
Observations 887 887
R-squared 0.53 0.49
Standard errors in brackets. *** p
Table 3: Sovereign Risk Determinants
Macro factors Financial Dominance? Including Bank Risk Contagion & Global Dynamic Panel - GMM
Public Debt (% GDP) 2.85074*** 3.03412*** 4.36466** 2.41267 -0.16140
[0.89192] [0.56061] [2.09820] [2.07418] [0.20781]
Inflation 41.88270** 42.31815*** 29.64377** 22.61933 0.44141
Fiscal Balance (% GDP) -0.89429 -0.55020
[20.46094] [5.00052] [14.16609] [14.55856] [1.41064]
Unemployment 24.68218** -12.20295*** -10.53978 -7.01676 -0.31433
[12.53592] [2.09126] [8.34691] [8.04618] [0.26713]
Financial account (% GDP) 8.77482** 7.80787*** 7.17697*** 8.14635*** 1.09265*
[4.14930] [1.20988] [1.80998] [1.64103] [0.58133]
GDP Growth -17.52449 -18.99286*** -14.83255 -18.74828 6.17804**
[23.69710] [7.22454] [20.55788] [21.74692] [2.41609]
VIX Index -0.91588 0.39047**
Other EA Sovereigns shock 0.39342*** 0.00061
Sovereign Risk 1.01593***
Bank Risk 0.39870*** 0.31215*** -0.07841***
[0.12477] [0.11033] [0.00990]
Bank Home Bias 11.00246 236.51432** 78.24319 -50.41017***
[59.50422] [92.86427] [107.01573] [15.36646]
Banks Private Assets 160.95964*** 107.23422* 58.04025 15.34203***
[18.25573] [57.96847] [54.51904] [4.45689]
Banks Assets to Deposits -236.85313*** -163.62463 -32.00543 -14.99333
[65.08768] [146.28101] [114.79124] [19.15749]
Banks funding from CB 5,198.06913*** 4,112.84212*** 4,519.80440*** -223.87460
[470.39164] [1,407.27665] [1,441.52373] [149.90186]
Non-performing loans 12.12065*** -7.57912 -3.34707 2.47547*
[2.26705] [7.11940] [7.22369] [1.27734]
Banks ROA 47.70184** 31.68372 31.27290 -14.87771**
[18.74357] [51.60539] [45.46797] [7.28199]
Banks' capital -3.18224 -12.52783 -15.01924
[3.26946] [13.04944] [13.30132]
Constant -412.95925*** -393.23746*** -474.62623*** -251.14994 33.93331
[97.57750] [104.77950] [147.34333] [169.74052] [30.87990]
Observations 819 543 543 543 534
R-squared 0.38 0.69 0.57 0.64
Sargan Test 163.8
Robust standard errors in brackets. *** p
TABLE 4: Bank Risk Determinants
Bank factors- ECB data
Bank factors - ECB & IMF
Bank & macro factors Including Sovereign Risk Contagion& Global Dynamic Panel - GMM
Bank Home Bias 645.29571*** -716.28140*** -593.35841*** -603.49704*** -632.80719*** -98.82991***
[145.31905] [72.68564] [59.86096] [51.56742] [50.16786] [17.39234]
Banks Private Assets 152.65238*** 159.25283*** 145.29523*** 61.91358*** 69.18806*** 16.86370*
[18.11861] [14.43979] [18.07193] [16.72809] [16.77667] [9.94408]
Banks Assets to Deposits -543.65014*** -68.74126 -204.20386*** -83.56846 -44.39201 -28.38901
[104.23514] [79.97998] [66.36337] [57.84573] [56.27863] [23.93555]
Banks funding from CB 6,595.00077*** 2,684.92650*** 2,528.43335*** -458.79641 324.59844 -194.33941**
[446.91417] [494.04588] [476.17691] [465.16681] [448.88955] [98.64389]
Non-performing loans 35.35430*** 48.81510*** 41.70719*** 43.35807*** 5.90579***
[2.61304] [2.31106] [2.05799] [1.94035] [0.54551]
Return on Assets 56.36885*** 19.19785 -16.29750 21.99971 -11.52246**
[19.51918] [18.90258] [16.48943] [16.14227] [5.11996]
Banks Capital 19.45874*** 20.19696*** 20.11718*** 21.42841*** 1.38871
[3.58217] [3.33560] [2.87317] [2.79773] [1.71676]
VIX Index 3.33184*** 0.74053***
Itraxx Junior Index 0.22615*** -0.01302
Bank Risk 0.84649***
Sovereign Risk 0.53566*** 0.43689*** 0.09855*
[0.03935] [0.04121] [0.05741]
Public Debt (% GDP) -3.37438*** -4.94036*** -4.46404*** -0.68918*
[0.56867] [0.50316] [0.51088] [0.41107]
Inflation 29.39950*** 6.72022 2.28081 -0.64970
[4.94414] [4.57294] [4.39152] [3.40228]
Unemployment -4.23686** 2.03623 2.90225 0.23715
[2.12738] [1.88949] [1.79895] [0.83246]
Financial account (% GDP) 2.40681** -1.23244 -1.74867* -0.07521
[1.20956] [1.07562] [1.03529] [0.30885]
GDP Growth -3.79514 9.13444 22.17504*** 10.21375***
[7.36727] [6.41658] [6.21383] [2.93831]
Constant -74.18393 -76.50793 295.24515*** 538.73613*** 256.14497*** 97.26295***
[181.85684] [121.33788] [106.88495] [93.78800] [98.68833] [34.31770]
873 543 543 543 543 534
Number of Observations 873 543 543 543 543 543
R-squared 0.31 0.79 0.84 0.87 .
Sargan Test 176.04
Robust standard errors in brackets. *** p
Table 5. Channels of transmission of Bank Risk
Dep. variable: Sovereign Risk
Bank Risk -0.06955*** -0.06905*** -0.06800***
Bank Risk* Banks' Size 0.00071***
Bank Risk* Non-performing
Bank Risk*Banks' Foreign
[0.01003] [0.01025] [0.01041]
Constant 81.07124*** 79.63427** 80.33793**
[31.02581] [31.08578] [31.29246]
Other controls Yes Yes Yes
Observations 534 534 534
Number of countries 9 9 9
Robust standard errors in brackets. *** p
Bank Risk -0.01432*** -0.01259*** -0.01180*** -0.01273***
Bank Risk* Bailout Size
(including contingent claims)
Table 7. Bank bailouts and feedback loops
[0.00319] [0.00258] [0.00275] [0.00247]
Dep. variable: Sovereign Risk
Bank Risk* Bailout Size 0.22592***
Bank Risk*Bailout Sizes*Banks'
Bank Risk*Bailout Size*Banks'
Constant -1.04590 -1.68672 12.64554*** -1.83684
[3.80581] [3.47236] [3.56339] [3.41779]
Other controls Yes Yes Yes Yes
Observations 534 534 534 534
Number of countries 9 9 9 9
Robust standard errors in brackets. *** p
6a Circuit de la Foire Internationale
Tel: +352 260 292 0
©European Stability Mechanism 09/2015